Multi-energy micro-grid double-layer optimization method and system for source-load-storage coupling of oil and gas well field

Abstract

According to the source-load-storage coupled multi-energy micro-grid double-layer optimization method and system for the oil and gas well field, an upper-layer model, namely a load optimization layer model with the purpose of maximizing the green power consumption rate, is constructed respectively, the working system and production parameters of production equipment are optimized, and the wind and light consumption level is enhanced while the production requirement is guaranteed; a lower-layer model, namely a power generation dispatching layer model taking the lowest daily operation cost of the multi-energy micro-grid as a target, is constructed, the energy storage peak regulation capability is fully exerted, and the economic benefit of system operation is improved; meanwhile, due to the fact that the coupling relation exists between the upper layer and the lower layer of the double-layer model, the double-layer nonlinear problem is converted into single-layer mixed integer linear programming through the KKT condition and Big-M linearization, the solving time is greatly shortened, and the method is suitable for a real-time scheduling scene. And the green electricity consumption rate and the well group daily fluid production capacity are respectively improved by 9.34% and 9.61%. And the double-target balance of economy and environmental protection is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas production, and in particular to a double-layer optimization method and system for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field. Background Art

[0002] Domestic and foreign oil and gas companies have already implemented clean energy practices such as photovoltaics, wind power, and geothermal energy, proving the feasibility of using solar energy and ocean thermal energy to power offshore oil and gas fields in the Caspian Sea.

[0003] However, research has primarily focused on optimizing energy consumption within a single production process (e.g., mechanical extraction and water injection), such as cluster well-to-well pumping scheduling and water injection pump start-stop strategies. Mathematical modeling has been used to optimize production parameters (output, well opening time, etc.) to reduce energy consumption, but this approach relies solely on traditional power grids, without considering renewable energy power supply scenarios or integrating them with renewable energy. Only a few studies have attempted to collaboratively optimize wind, solar, and energy storage microgrids with clusters of pumping wells (e.g., staggered pumping and idealized power curve modeling), but this has not covered the entire production process, including water injection and gathering and transportation. While models such as photovoltaic-electric microgrids and wind, solar, and energy storage capacity configuration have been proposed, there is a lack of collaborative optimization of the entire source-load-storage system.

[0004] Therefore, it is necessary to design a two-layer optimization method and system for a multi-energy microgrid with source-load-storage coupling in oil and gas well fields, build a model algorithm to solve the model of the multi-energy microgrid with source-load-storage coupling, and solve the problems of insufficient green electricity absorption capacity and poor operating economic benefits of the multi-energy microgrid oil and gas production system during operation. Summary of the Invention

[0005] In view of the analysis of background technology, the present invention provides a two-layer optimization method and system for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field, which can reduce the system operating costs while ensuring production needs, reduce dependence on power purchases from the power grid, and enhance the system's green electricity absorption capacity.

[0006] To achieve the above and other related objectives, the present invention provides a two-layer optimization method and system for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field, comprising:

[0007] A two-layer optimization method for a multi-energy microgrid coupled with a source, load and storage in an oil and gas well field comprises the following steps:

[0008] Based on oil and gas production demand and equipment operating parameter constraints, a load optimization layer model is constructed with the goal of maximizing the green electricity consumption rate, and the equipment operation scheduling plan is solved;

[0009] Based on the electricity load data generated by the operation and scheduling plan, combined with wind and solar power generation forecast data and oil field time-of-use electricity price signals, a power generation scheduling layer model is constructed with the goal of minimizing the daily operation cost of the multi-energy microgrid.

[0010] The load optimization layer model and the power generation scheduling layer model are solved by a two-level planning coupling algorithm to obtain an optimal scheduling scheme that includes equipment start-stop timing, energy storage charging and discharging strategies, and oilfield-grid interaction power.

[0011] In one embodiment of the present invention, the two-level planning coupling algorithm includes:

[0012] The generation scheduling layer model is transformed into the linear constraint conditions of the load optimization layer model through KKT conditions and Big-M linearization method;

[0013] The single-level mixed integer linear programming (MILP) algorithm is used to solve the load optimization layer model.

[0014] In one embodiment of the present invention, the objective function of the load optimization layer model is:

[0015]

[0016] Among them, R is the green electricity consumption rate; P load (t) is the electricity consumption of oil and gas production at time t; P buy (t) is the electricity purchased from the oil field at time t; P PV (t) is the output power of the photovoltaic unit at time t; P WT (t) is the output power of the wind turbine at time t.

[0017] In one embodiment of the present invention, the constraints of the load optimization layer model specifically include:

[0018] Operational constraints for oil and gas production and extraction systems, including single-well productivity constraints, single-well daily total production constraints, well group daily total production constraints, gathering and transportation pipeline flow constraints, single-well daily opening time constraints, well group opening quantity constraints for each time period, and pumping unit operating power constraints;

[0019] Operational constraints of the oil and gas production water injection system, including water supply and injection volume balance constraints, injection well pressure constraints, injection station water supply constraints, and injection pump displacement constraints;

[0020] The operational constraints of the oil and gas production and gathering system include the heat load constraints of the heating furnace, the flow constraints of the water injection pump, and the pressure constraints of the water injection pipeline.

[0021] In one embodiment of the present invention, the operating power constraints of the pumping unit include: P i min ≤P i (t)≤P i max , where P i min and P i max are the minimum and maximum output power of the photovoltaic system respectively.

[0022] In one embodiment of the present invention, the objective function of the power generation scheduling model is:

[0023] C day is the daily operating cost of the multi-energy microgrid; P Bat (t) is the energy storage output power at time t; P buy (t) and P sell (t) are the electricity purchased from the oilfield power grid and the electricity sold to the oilfield power grid at time t; K PV , K WT and K Bat are the operating costs of photovoltaic units and wind turbines, and the energy storage maintenance costs; K buy (t) and K sell (t) are the electricity prices for purchasing and selling electricity from the oil field power grid at time t; T is the scheduling period.

[0024] In one embodiment of the present invention, the constraints of the power generation scheduling model include:

[0025] Photovoltaic unit power constraints, wind turbine unit power constraints, oil field and grid interaction power constraints, energy storage state of charge (SOC) and energy storage charging and discharging power constraints.

[0026] In one embodiment of the present invention, the constraints of the power generation scheduling layer model also include power balance constraints, which include photovoltaic units, wind turbines, energy storage batteries and oil field power grids as power sources, and their output power meets the power demand of oil and gas production mining systems, oil and gas production water injection systems and oil and gas production gathering and transportation systems.

[0027] To achieve the above-mentioned and other related purposes, the present invention further provides a multi-energy microgrid dual-layer optimization system for oil and gas well fields with source-load-storage coupling, comprising:

[0028] The load optimization unit, based on oil and gas production demand and equipment operating parameter constraints, builds a load optimization layer model with the goal of maximizing the green electricity consumption rate, and solves the equipment operation scheduling plan;

[0029] The power generation dispatch unit, based on the power load data generated by the operation and dispatch plan, combines wind and solar power generation power forecast data and oil field power grid time-of-use electricity price signals to build a power generation dispatch layer model with the goal of minimizing the daily operating cost of the multi-energy microgrid;

[0030] The coupling unit solves the load optimization layer model and the power generation scheduling layer model through model coupling, and obtains the optimal scheduling plan that includes equipment start-stop timing, energy storage charging and discharging strategy, and oilfield-grid interaction power.

[0031] In one embodiment of the present invention, a multi-energy microgrid includes:

[0032] The energy supply subsystem includes photovoltaic units, wind turbines, and an oilfield power grid. The photovoltaic units and wind turbines are connected to the DC bus through a grid-connected controller, and the oilfield power grid is connected to the DC bus through a transformer.

[0033] Production load subsystem, the pumping units of the production load subsystem are connected to the DC bus through an inverter;

[0034] Energy storage subsystem, energy storage subsystem and energy supply subsystem are connected;

[0035] The microgrid control cabinet executes the optimal scheduling scheme of the multi-energy microgrid two-layer optimization system in real time through the energy interlocking decision maker.

[0036] To address the problems of poor economic benefits and insufficient green electricity absorption capacity faced by oil and gas production systems that introduce wind, solar, and storage multi-energy microgrids, the present invention proposes a two-layer optimization method and system for a multi-energy microgrid coupled with source, load, and storage at an oil and gas well site, achieving at least the following beneficial technical effects:

[0037] (1) The present invention provides a two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field. The upper layer model, i.e., the load optimization layer model with the goal of maximizing the green electricity consumption rate, is constructed to optimize the working system and production parameters of the production equipment, thereby ensuring production requirements and enhancing the wind and solar power consumption level. The lower layer model, i.e., the power generation scheduling layer model with the goal of minimizing the daily operating cost of the multi-energy microgrid is constructed to give full play to the peak-shaving capacity of energy storage and improve the economic benefits of system operation. At the same time, due to the coupling relationship between the upper and lower layers of the two-layer model, the present invention converts the two-layer nonlinear problem into a single-layer mixed integer linear programming (MILP) through KKT conditions and Big-M linearization, which greatly shortens the solution time and is suitable for real-time scheduling scenarios.

[0038] (2) The present invention is a multi-energy microgrid dual-layer optimization system for oil and gas well fields with source, load and storage coupling. When establishing a dual-layer collaborative optimization model for wind and solar new energy power generation, battery energy storage and oil and gas production systems with the goal of minimizing daily operating costs and maximizing green electricity consumption rate, the electricity demand of the oil and gas production system is met through coordinated scheduling between energy storage batteries and power purchase and sales by the power grid, and the economic benefits of system operation are improved, and the green electricity consumption capacity is enhanced. Among them, the battery plays a role in peak shaving and valley filling for wind and solar new energy power generation, smoothing out the fluctuation of wind and solar power, ensuring the stability and reliability of system operation, and giving priority to power supply over the power grid, reducing dependence on power purchase from the power grid, reducing system operation costs, and improving green electricity consumption capacity. Compared with the system without battery consideration, although the daily operating cost increased by 33.98%, the power grid fluctuation was smaller, which is conducive to the stability of system operation. The green electricity consumption rate and daily liquid production of the well group increased by 9.34% and 9.61% respectively. In summary, the present invention achieves a balance between economic and environmental protection goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 This is a flow chart of a two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field according to one embodiment of the present invention;

[0041] Figure 2 This is a specific flow chart of a multi-energy microgrid power supply and distribution strategy in one embodiment of the present invention;

[0042] Figure 3 This is a specific solution flow chart of the double-level programming coupling algorithm in one embodiment of the present invention;

[0043] Figure 4 This is a system diagram of a multi-energy microgrid dual-layer optimization system for source-load-storage coupling in an oil and gas well field according to one embodiment of the present invention;

[0044] Figure 5 This is a structural diagram of a multi-energy microgrid in one embodiment of the present invention;

[0045] Figure 6 This is a schematic diagram of wind and solar power day-ahead power prediction in one embodiment of the present invention;

[0046] Figure 7 Schematic diagram of the day-ahead scheduling plan of wind-solar power generation and pumping system between pumping wells in one embodiment of the present invention;

[0047] Figure 8This is a schematic diagram of the operating status of a pumping well group in one embodiment of the present invention;

[0048] Figure 9 Schematic diagram of the state of charge (SOC) of a battery in one embodiment of the present invention;

[0049] Figure 10 Schematic diagram of the change of liquid production of a group of pumping wells in one embodiment of the present invention;

[0050] Figure 11 Schematic diagram of the system day-ahead scheduling plan when there is no energy storage battery in one embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following describes the embodiments of the present invention through specific examples. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention.

[0052] It should be noted that the illustrations provided in this embodiment are only used to schematically illustrate the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0053] See also Figures 1 to 6 See Figure 1 To achieve the above-mentioned and other related purposes, the present invention provides a two-layer optimization method for a multi-energy microgrid coupled with a source, load and storage in an oil and gas well field, comprising the following steps:

[0054] Step (1): Based on the oil and gas production demand and equipment operating parameter constraints, a load optimization layer model with the goal of maximizing the green electricity consumption rate is constructed to obtain the equipment operation scheduling plan;

[0055] Step (2): Based on the electricity load data generated by the operation and scheduling scheme, combined with the wind and solar power generation power forecast data and the oil field time-of-use electricity price signal, a power generation scheduling layer model is constructed with the goal of minimizing the daily operation cost of the multi-energy microgrid;

[0056] Step (3): Solve the load optimization layer model and the power generation scheduling layer model through the two-layer planning coupling algorithm to obtain the optimal scheduling scheme including equipment start-stop timing, energy storage charging and discharging strategy, and oilfield power grid interaction power.

[0057] It should be noted that the multi-energy microgrid two-layer optimization method of the present invention dynamically matches production constraints (oil and gas production requirements and equipment operating parameters) with the multi-energy microgrid power supply strategy, and realizes source-load-storage coordinated optimization through a two-layer model (load optimization layer model + power generation scheduling layer model). Resolve the contradiction between the intermittent nature of wind and solar power generation and the rigid demand of production load. Make the production load actively adapt to the wind and solar power generation curve; the energy storage charging and discharging strategy under the SOC (battery remaining power indicator) constraint gives priority to absorbing green electricity, and finally the remaining electricity is connected to the grid, which greatly improves the green electricity absorption rate. The load optimization layer achieves the goal of maximizing the green electricity absorption rate, and the scheduling optimization layer achieves the goal of minimizing the daily operating cost, thereby reducing the overall operating cost.

[0058] For further information, see Figure 2 The specific process of the multi-energy microgrid power supply and distribution strategy includes:

[0059] When wind and solar power generation exceeds production load power demand, energy storage charging is performed, and the energy storage SOC is checked to see if it is lower than SOC_max. If the energy storage SOC is lower than SOC_max, the energy storage battery is charged. This allows excess energy to be stored for subsequent use, improving the utilization rate of renewable energy.

[0060] If the energy storage is full, that is, SOC ≥ SOC_max, the excess energy will be fed back to the oilfield power grid. Revenue is generated through electricity sales, avoiding wind and solar power curtailment.

[0061] When wind and solar power generation is less than the production load power demand, the energy storage is discharged and the energy storage SOC is checked to see if it is higher than SOC_min. If the SOC is higher than SOC_min, the energy storage battery is discharged to fill the power gap. This allows the priority use of low-cost energy storage power and reduces dependence on the grid.

[0062] If the energy storage is insufficient (i.e., SOC ≤ SOC_min), electricity is purchased from the oilfield power grid. This ensures production continuity, but at a higher cost.

[0063] When dynamic adjustments are made based on wind and solar power generation forecasts and the load curve of the oil and gas production system, an optimized scheduling plan is developed, including energy storage charging and discharging strategies (such as charging during off-peak hours and discharging during peak hours), equipment start-up and shutdown timing (i.e., production load scheduling) (such as delaying non-emergency operations until wind and solar power are sufficient), and oilfield-grid interaction power. This minimizes the total cost per cycle (power purchase cost + energy storage loss + power curtailment penalty).

[0064] In one embodiment of the present invention, the two-level planning coupling algorithm includes the following steps:

[0065] Step (1): The generation scheduling layer model is transformed into the linear constraint conditions of the load optimization layer model through KKT conditions and Big-M linearization method;

[0066] Step (2): Use the single-layer mixed integer linear programming algorithm (MILP) to solve the load optimization layer model.

[0067] Further, see Figure 3 As shown in Figure 2, the specific solution process of the two-level programming coupling algorithm includes:

[0068] Input parameters: Input data such as wind and solar power generation forecasts, oil field grid electricity prices, production demand (oil and gas production), etc.; build the upper model (load optimization layer model) and the lower model (power generation scheduling layer model).

[0069] Two-layer coupling problem transformation: The optimization problem of the lower-layer model is transformed into its KKT conditions (including primal feasibility, dual feasibility, and complementary relaxation conditions) as additional constraints for the upper-layer model.

[0070] Nonlinear term processing: The nonlinear terms introduced by the complementary relaxation condition are linearized using the Big-M method;

[0071] Single-level mixed integer linear programming (MILP) generation: merging the upper-level model objective function with the linearized lower-level constraints;

[0072] Solution: Call the Gurobi solver in the PyCharm environment; output optimization results including equipment scheduling plans, energy storage charging and discharging curves, and electricity purchase and sales strategies.

[0073] It can be seen that the KKT condition and Big-M method have made the complex problems computable, providing an efficient tool for the real-time economic dispatch of multi-energy microgrids.

[0074] In one embodiment of the present invention, the objective function of the load optimization layer model is:

[0075]

[0076] Among them, R is the green electricity consumption rate; P load (t) is the electricity consumption of oil and gas production at time t; P buy (t) is the electricity purchased from the oil field at time t; P PV (t) is the output power of the photovoltaic unit at time t; P WT (t) is the output power of the wind turbine at time t.

[0077] In one embodiment of the present invention, the constraints of the load optimization layer model specifically include:

[0078] Operational constraints for oil and gas production and extraction systems, including single-well productivity constraints, single-well daily total production constraints, well group daily total production constraints, gathering and transportation pipeline flow constraints, single-well daily opening time constraints, well group opening quantity constraints for each time period, and pumping unit operating power constraints;

[0079] It should be noted that the single well production capacity constraint is: in order to meet production demand, the production of a single well at each moment cannot drop to a certain critical value, otherwise the well needs to be shut down to restore production capacity. It can be expressed as: is the minimum production capacity of single well i;

[0080] Single well daily production constraint: The daily total production of an oil well within the scheduling cycle must be greater than the minimum production requirement of the well. Generally, the daily cumulative production after intermittent pumping is required to be basically the same as that before intermittent pumping. It can be expressed as: is the minimum daily production of single well i.

[0081] Well group daily total production constraint: Ensure that the daily production of the well group is not less than the lower limit of the well group daily production. It can be expressed as: L min It is the minimum daily production of the pumping well group.

[0082] Gathering pipeline flow constraint: This is to prevent the total production of the well group from being insufficient in each period, which may lead to freezing of the oil gathering pipeline, or excessive pressure in the pipeline due to excessive production. It can be expressed as:

[0083] F min and F max They are the minimum and maximum flow rates allowed in the gathering and transportation pipelines.

[0084] Single well daily operating time constraint: To avoid wax deposition in oil wells and pipeline freezing and blockage, and to ensure the stability of single well daily production, the total daily operating time of each well needs to be limited. This can be expressed as:

[0085] O min and O max are the minimum and maximum daily drilling time of a single well, respectively.

[0086] Well group operation status constraints: To ensure that the flow rate is within a controllable range, prevent pipeline freezing and blockage, and prevent excessive load on the multi-energy microgrid due to too many open wells, which may impact the power grid, the number of open wells in the well group cannot exceed a certain range at any time. This can be expressed as:

[0087] P i min and P i max are the minimum and maximum output power of the photovoltaic system respectively.

[0088] The operating power constraints of the pumping unit include: P i min ≤P i (t)≤P i max, where P i min and P i max are the minimum and maximum output power of the photovoltaic system respectively.

[0089] Operational constraints of the oil and gas production water injection system, including water supply and injection volume balance constraints, injection well pressure constraints, injection station water supply constraints, and injection pump displacement constraints;

[0090] It should be noted that the water supply and injection balance constraint is: the total water supply should be equal to the sum of the injection volumes of each well, which can be expressed as:

[0091] μ ij and β ij is the displacement and start / stop status of the jth injection pump in the i-th injection station (0-1 variable); m represents the number of injection stations; n pi is the number of water injection pumps in the i-th water injection station; N w is the total number of injection wells; Q j is the injection volume of the j-th injection well.

[0092] Injection well pressure constraint: To ensure that incoming water can be smoothly injected into the formation, the incoming water pressure of each injection well must not be less than the required minimum injection pressure, which can be expressed as: P i min The minimum injection pressure required for the j-th injection well to meet the service quality requirements.

[0093] Water supply constraint of water injection station: The injection volume of all water injection pumps working in each water injection station should be less than the maximum water inflow of this station. That is, the discharge volume of each water injection station should be within its water supply capacity, which can be expressed as: and are the maximum and minimum water inflow of the i-th water injection station respectively.

[0094] Injection pump displacement constraint: Each injection pump must be limited to operate within the high-efficiency working area, which can be expressed as: and are the maximum flow rate and minimum flow rate of the jth water injection pump in the i-th water injection station working in the efficient working area.

[0095] The operational constraints of the oil and gas production and gathering system include the heat load constraints of the heating furnace, the flow constraints of the water injection pump, and the pressure constraints of the water injection pipeline.

[0096] Heating furnace working characteristic constraints: To ensure the safe and efficient operation of the heating furnace, the heating furnace heat load R should be within a certain range, so the heating furnace heat load constraint condition is R min ≤R≤Rmax , R min is the lower limit of the heating furnace heat load, R max The upper limit of the heating furnace heat load. The heating furnace inlet temperature is generally based on the ambient temperature T0, which can be calculated by the heat load formula: R = Q c ·ρ w c w (T out -T0) is derived from the upper and lower limits of the heating furnace outlet temperature, then the heating furnace outlet temperature T out The constraints are: T min ≤T out ≤T max , T min is the lower limit of the outlet temperature of the heating furnace, T min It is the upper limit of the outlet temperature of the heating furnace.

[0097] Constraints on the working characteristics of the water injection pump: From the working characteristic curve of the centrifugal pump, it can be seen that in order to ensure the safe and efficient operation of the centrifugal pump, the displacement of the centrifugal pump must be controlled within a certain range, generally within the efficient working range of the centrifugal pump. That is: Q max , Q min are the upper and lower limits of the flow rate in the high-efficiency zone of the water injection pump; q сi is the water mixing amount of a single water mixing pump; n is the number of water mixing pumps.

[0098] Water injection pipeline pressure constraint: In the water injection oil gathering process, the pressure at the end of the water injection pipeline should be higher than the wellhead oil pressure to ensure the smooth progress of the water injection process. That is: [P M ] is the minimum allowable water injection pressure; S MP is the water injection pressure constraint set; P Mmi The water mixing pressure.

[0099] In one embodiment of the present invention, the objective function of the power generation scheduling model is:

[0100] C day is the daily operating cost of the multi-energy microgrid; P Bat (t) is the energy storage output power at time t; P buy (t) and P sell (t) are the electricity purchased from the oilfield power grid and the electricity sold to the oilfield power grid at time t; K PV , K WT and K Bat are the operating costs of photovoltaic units and wind turbines, and the energy storage maintenance costs; K buy (t) and K sell (t) are the electricity prices for purchasing and selling electricity from the oil field power grid at time t; T is the scheduling period.

[0101] In one embodiment of the present invention, the constraints of the power generation scheduling layer model include: photovoltaic unit power constraints, wind turbine unit power constraints, oil field grid interaction power constraints, energy storage state of charge (SOC) and energy storage charging and discharging power constraints.

[0102] It should be noted that the upper and lower limit constraints of photovoltaic unit power can be expressed as:

[0103] and are the minimum and maximum output power of the photovoltaic system respectively.

[0104] The upper and lower limit constraints of wind turbine power can be expressed as:

[0105] and are the minimum and maximum output power of the wind turbine respectively.

[0106] The upper and lower limit constraints of grid interaction power can be expressed as: P grid (t) is the interactive power of the power grid at time t; and are the minimum and maximum values of grid interaction power, respectively.

[0107] The energy storage state of charge (SOC) and energy storage charge and discharge power constraints can be expressed as:

[0108] When the battery is charging:

[0109] When the battery is discharged:

[0110] Battery remaining capacity SOC upper and lower limit constraints: SOC min ≤SOC(t)≤SOC max , SOC min and SOC max They are the minimum and maximum values of the battery's remaining capacity SOC.

[0111] To ensure the periodicity of power system dispatch, the initial state of charge of the battery is set to be equal to the state of charge at the last moment in a cycle: SOC(t=0)=SOC(t=T);

[0112] Battery charging and discharging power upper and lower limit constraints:

[0113] and are the maximum values of battery charging power and discharging power respectively; UBat,ch (t) and U Bat,dis (t) is the binary flag of battery charging and discharging at time t, with the value of 0 or 1.

[0114] In one embodiment of the present invention, the constraints of the power generation scheduling layer model also include power balance constraints, which include photovoltaic units, wind turbines, energy storage batteries and oil field power grids as power sources, and their output power meets the power demand of oil and gas production mining systems, oil and gas production water injection systems and oil and gas production gathering and transportation systems.

[0115] It should be noted that the power balance constraint can be expressed as:

[0116] P PV (t)+P WT (t)+P grid (t)+U Bat,dis P Bat,dis (t)-U Bat,ch (t)P Bat,ch (t) = P load (t), P load (t) is the electricity consumption at the oil and gas production end at time t.

[0117] See Figure 4 To achieve the above-mentioned and other related purposes, the present invention further provides a multi-energy microgrid dual-layer optimization system for oil and gas well fields with source-load-storage coupling, comprising:

[0118] The load optimization unit, based on oil and gas production demand and equipment operating parameter constraints, constructs a load optimization layer model with the goal of maximizing the green electricity consumption rate, and solves the equipment operation scheduling plan.

[0119] The power generation dispatching unit, based on the power load data generated by the operation dispatching plan, combines the wind and solar power generation power forecast data and the oil field power grid time-of-use electricity price signal, to build a power generation dispatching layer model with the goal of minimizing the daily operation cost of the multi-energy microgrid.

[0120] The coupling unit solves the load optimization layer model and the power generation scheduling layer model through model coupling, and obtains the optimal scheduling plan that includes equipment start-stop timing, energy storage charging and discharging strategy, and oilfield-grid interaction power.

[0121] It should be noted that the upper layer is the load optimization model, while the lower layer is the power generation scheduling model. The upper layer optimizes the oil and gas production system's daily green power consumption rate, taking into account the operational constraints of production equipment and related production constraints, to optimize the oil and gas production system's operating plan and equipment parameters. The lower layer optimizes the multi-energy microgrid's daily operating cost, taking into account the output constraints of each device and the energy storage battery, and optimizes the energy storage battery charging and discharging strategy, the oilfield-grid power interaction power load, and the device output to achieve economic scheduling of the multi-energy microgrid. The upper layer first optimizes the operating plan based on the production requirements and operating models of each oil and gas production system. It then transmits the optimized load information to the lower layer. The lower layer optimizes the multi-energy microgrid's scheduling plan based on wind and solar forecast data, the load data transmitted from the upper layer, and the equipment output model, and returns the result to the upper layer. Through mutual feedback and repeated iterations of coupled information, the optimal solution, or the optimized scheduling plan, is achieved.

[0122] See Figure 5 As shown, in one embodiment of the present invention, a multi-energy microgrid includes an energy supply subsystem, a production load subsystem, an energy storage subsystem, and a microgrid control cabinet. The energy supply subsystem includes photovoltaic units, wind turbines, and an oilfield power grid. The photovoltaic units and wind turbines are connected to the DC bus via a grid-connected controller, and the oilfield power grid is connected to the DC bus via a transformer. The pumping units in the production load subsystem are connected to the DC bus via an inverter. The energy storage subsystem is connected to the energy supply subsystem. The microgrid control cabinet uses an energy interlocking decision maker to execute the optimal scheduling plan of the multi-energy microgrid's two-tier optimization system in real time.

[0123] It should be noted that the source end, i.e., the energy supply subsystem, is mainly powered by a combination of photovoltaic units and wind turbines. The energy storage subsystem includes energy storage batteries and plays a role in peak shaving and valley filling, smoothing out fluctuations in wind and solar power generation, and taking priority over the oilfield power grid in power supply. The load end refers to the intermittently pumping well group of the production load subsystem (including the oil and gas production system, which can include the oil and gas production mechanical extraction system, the oil and gas production water injection system, and the oil and gas production gathering and transportation system). Excess wind and solar power that cannot be absorbed by the energy storage batteries can be transmitted to the oilfield power grid. When wind and solar power generation is insufficient and the remaining power of the energy storage batteries is insufficient and can no longer supply power, power can be drawn from the oilfield power grid.

[0124] To minimize the impact of reverse power generation from pumping units on the stable operation of the power grid, DC bus technology is often used in actual production processes to achieve energy recirculation and recycling. DC bus group control technology involves multiple wells sharing a single transformer and rectifier, converting 380V three-phase AC power into 540V two-phase DC power, which is then transmitted to each well via a DC bus. This technology not only simplifies system configuration but also, when one or more motors in the production system are in reverse power generation, it transfers this energy to other motors in the same system for electrical consumption, effectively reducing the impact of motor reverse power generation on the oilfield power grid.

[0125] Example 1: The production data of the cluster well group YXX in Changqing Oilfield is selected for case analysis. The well group contains 10 pumping wells, the installed capacity of the wind turbine is 20KW, and the installed capacity of the photovoltaic is 25KW. The day-ahead power forecast of the wind turbine and photovoltaic at the source end is as follows: Figure 6 As shown in Table 1, the upper limit for electricity purchases and sales from the high-voltage power grid is 20 kW. The oilfield power grid uses a time-of-use electricity pricing mechanism, as shown in Table 1. The 24-hour day is divided into three periods: peak, off-peak, and normal, and electricity prices are calculated for each period. During peak hours, electricity prices are higher due to tight power supply, such as during the day. During off-peak hours, electricity demand is lower and power supply is sufficient, resulting in lower prices, such as at night. The parameters related to the energy storage battery and the economic cost parameters of each device are shown in Table 2 (energy storage battery parameters) and Table 3 (economic cost parameters for wind turbines, photovoltaic units, and energy storage subsystems). The single well scheduling parameters are shown in Table 4, and the parameter limits for the load-side constraints are shown in Table 5.

[0126] Table 1

[0127]

[0128] Table 2

[0129]

[0130] Table 3

[0131]

[0132] Table 4

[0133]

[0134]

[0135] Table 5

[0136]

[0137] The scheduling cycle is 24 hours, with daily scheduling determined at one-hour intervals. To evaluate the performance of the economic operation model for the inter-pumping system between wind and solar power generation and a group of pumping wells, the PyCharm+Gurobi solver was used to optimize the inter-pumping schedule for the group of pumping wells, using the operating status of the group of pumping wells as the decision variable.

[0138] The liberalized scheduling scheme is as follows: Figure 7 The figure shows the day-ahead scheduling plan for the off-peak pumping system of photovoltaic, wind turbines, and oil pumping wells. It can be seen that the daily operating cost of the multi-energy microgrid is 150.19 yuan, the green power consumption rate is 86.08%, and the daily liquid production of the well group is 37.42m 3 .

[0139] Figure 8 and Figure 9 They are respectively the changes in the operating status of the pumping well group and the SOC changes of the energy storage battery when the above-mentioned optimal scheduling plan is implemented. Figure 7 As can be seen, between 1:00 AM and 6:00 AM and 7:00 PM and 9:00 PM, due to low or even no photovoltaic power generation, the wind turbines were unable to independently meet the power needs of the oil wells. During these times, the energy storage batteries were discharged, serving as a supplemental power source. Between 11:00 AM and 4:00 AM, the photovoltaic units generated too much power, and the combined power generation of photovoltaics and wind turbines exceeded the power consumption of the oil wells. During this time, the energy storage batteries were charged to store the excess green power. Due to the sufficient power generation of the photovoltaic units and wind turbines, the demand for electricity purchased from the high-voltage grid was relatively low. Only between 7:00 AM and 10:00 AM did the energy storage batteries remain discharged.

[0140] from Figure 9 It can be seen that during this time period, the remaining charge (SOC) of the energy storage battery has dropped to near its minimum value, making it unable to discharge further to supply the well cluster. At this time, wind and solar power generation still cannot meet the power demand of the pumping well cluster, so it is necessary to purchase electricity from the grid to supplement the supply. At midnight, due to the high wind turbine generation, if the well cluster is not operating, the well cluster will not be able to absorb the wind turbine green power, thus reducing the green power absorption rate of the well cluster. Furthermore, the remaining charge (SOC) of the energy storage battery needs to be restored to its pre-dispatch state to ensure normal dispatch the next day. Therefore, a small amount of electricity is purchased from the grid to power the well cluster and charge the energy storage battery to ensure that the remaining charge is restored to its initial value before dispatch. During system operation, the energy storage battery effectively performs the role of "peak shaving" and "valley filling", smoothing out fluctuations in wind and solar power generation and improving system stability and reliability. Furthermore, the energy storage battery takes priority over the high-voltage grid in power supply, reducing reliance on power purchases from the high-voltage grid and effectively lowering the economic cost of system operation.

[0141] like Figure 10 Figure 2 shows how the fluid production of a pumping well cluster changes over time. Due to the abundant wind and solar power generation, and the presence of energy storage batteries that "shaving peaks and filling valleys," the pumping well cluster can fully utilize clean electricity, increase the number of wells in operation, and boost production. Overall, the cluster's fluid production and power consumption vary with wind and solar power generation, meeting daily production needs and enhancing the cluster's green electricity consumption capacity.

[0142] Example 2: As a comparative example of Example 1,

[0143] Example 2 is a case where energy storage batteries are not considered. The day-ahead optimization scheduling results are analyzed and compared with this one. The indicators for day-ahead optimization scheduling under the two schemes of Example 1 and Example 2 are shown in Table 6.

[0144] Table 6

[0145]

[0146] Table 6 shows that compared to the case with energy storage batteries, the absence of energy storage batteries, which lack the "peak shaving" function, means that wind and solar power are underutilized, resulting in a 9.34% decrease in the green power consumption rate. When wind and solar power generation is insufficient, electricity must be purchased from the high-voltage grid to meet the power needs of the pumping wells. This prevents large-scale well operation, resulting in a 9.61% decrease in daily fluid production.

[0147] like Figure 11 As shown in the figure, without energy storage batteries, excess wind and solar power is sold directly to the high-voltage grid for economic benefits, and there are no energy storage maintenance costs. Compared to the case with energy storage batteries, system operating costs are reduced by 33.98%. However, the lack of energy storage batteries to smooth out fluctuations in wind and solar power results in large fluctuations in grid power, seriously affecting the stability and reliability of system operation.

[0148] The above analysis of system scheduling optimization results and the comparison of optimization results with and without energy storage batteries indicate that the dual-layer optimization method and system for multi-energy microgrids of the present invention can effectively increase the multi-energy microgrid's capacity to absorb wind, solar, and green power, reduce its costs, and improve its economic benefits. By utilizing the peak-shaving and valley-filling capabilities of energy storage batteries, they effectively smooth out fluctuations in wind, solar, and green power, reduce reliance on grid power purchases, prevent large fluctuations in grid power, and enhance the stability and reliability of the multi-energy microgrid's operation.

[0149] Current research focuses solely on integrating wind and solar power into oil and gas production systems. In the future, clean energy sources such as geothermal and hydrogen will be incorporated into these systems, building a more comprehensive oil and gas production energy system dominated by renewable energy and establishing a more comprehensive collaborative scheduling optimization model. Furthermore, research is needed to refine the optimization scheduling algorithms applicable to this scenario, increasing their accuracy and computational speed.

[0150] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

[0151] In the description herein, numerous specific details, such as examples of components and / or methods, are provided to provide a complete understanding of the embodiments of the present invention. However, those skilled in the art will recognize that embodiments of the present invention may be practiced without one or more of the specific details or with other devices, systems, assemblies, methods, components, materials, parts, etc. In other cases, well-known structures, materials, or operations are not specifically shown or described in detail to avoid obscuring aspects of the embodiments of the present invention.

[0152] Reference throughout this specification to "one embodiment," "an embodiment," or "a specific embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention, and not necessarily in all embodiments. Thus, various appearances of the phrases "in one embodiment," "in an embodiment," or "in a specific embodiment" in different places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics of any specific embodiment of the invention may be combined with one or more other embodiments in any suitable manner. It should be understood that other variations and modifications of the embodiments of the invention described and illustrated herein are possible in light of the teachings herein and are considered part of the spirit and scope of the invention.

[0153] It should also be understood that one or more of the elements shown in the figures may also be implemented in a more separate or more integrated manner, or even removed because they are inoperable in certain circumstances or provided because they may be useful depending on the application.

[0154] In addition, unless otherwise expressly indicated, any marking arrows in the drawings should be regarded as illustrative only and not limiting. Furthermore, unless otherwise indicated, the term "or" as used herein is generally intended to mean "and / or." Where a term is unclear in providing separation or combination capabilities, the combination of components or steps will also be considered as indicated.

[0155] As used in the description herein and throughout the claims that follow, “a,” “an,” and “the” include plural references unless otherwise indicated. Likewise, as used in the description herein and throughout the claims that follow, the meaning of “in” includes “in” and “on” unless otherwise indicated.

[0156] The above description of the illustrated embodiments of the present invention (including that described in the Abstract) is not intended to be exhaustive or to limit the invention to the precise forms disclosed herein. Although specific embodiments of the present invention and examples of the present invention are described herein for illustrative purposes only, as those skilled in the art will recognize and appreciate, various equivalent modifications are possible within the spirit and scope of the present invention. As noted, modifications may be made to the present invention in light of the above description of the illustrated embodiments of the present invention, and such modifications will be within the spirit and scope of the present invention.

[0157] Systems and methods have been generally described herein in detail to facilitate understanding of the present invention. In addition, various specific details have been given to provide an overall understanding of embodiments of the present invention. However, those skilled in the relevant art will recognize that embodiments of the present invention may be practiced without one or more of these specific details, or with other devices, systems, accessories, methods, components, materials, parts, etc. In other cases, well-known structures, materials, and / or operations are not specifically shown or described in detail to avoid obscuring aspects of embodiments of the present invention.

[0158] Thus, although the invention has been described herein with reference to specific embodiments thereof, freedom of modification, various changes and substitutions are contemplated within the foregoing disclosure, and it should be understood that in some cases, some features of the invention will be employed without the corresponding use of other features without departing from the scope and spirit of the claimed invention. Thus, many modifications may be made to adapt a particular environment or material to the true scope and spirit of the invention. The invention is not intended to be limited to the specific terminology used in the claims below and / or to the specific embodiments disclosed as the best mode contemplated for carrying out the invention, but the invention is intended to include any and all embodiments and equivalents falling within the scope of the appended claims. Thus, the scope of the invention will be determined solely by the appended claims.

Claims

1. A two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field, characterized in that: The following steps are involved: Based on oil and gas production demand and equipment operating parameter constraints, a load optimization layer model is constructed with the goal of maximizing the green electricity consumption rate, and the equipment operation scheduling plan is solved; Based on the operation and dispatch plan, electricity load data is generated. Combined with wind and solar power generation forecast data and oil field time-of-use electricity price signals, a power generation dispatch layer model is constructed with the goal of minimizing the daily operation cost of the multi-energy microgrid. The load optimization layer model and the power generation scheduling layer model are solved by a two-level planning coupling algorithm to obtain an optimal scheduling scheme that includes equipment start-stop timing, energy storage charging and discharging strategies, and oilfield-grid interaction power.

2. According to claim 1, a multi-energy microgrid dual-layer optimization method for oil and gas well field source-load-storage coupling is characterized in that: The coupled algorithm through bi-level programming includes: The generation scheduling layer model is transformed into the linear constraint conditions of the load optimization layer model through KKT conditions and Big-M linearization method; A single-level mixed integer linear programming algorithm is used to solve the load optimization layer model.

3. According to claim 1, a two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field, characterized in that: The objective function of the load optimization layer model is: Among them, R is the green electricity consumption rate; P load (t) is the electricity consumption of oil and gas production at time t; P buy (t) is the electricity purchased from the oil field at time t; P PV (t) is the output power of the photovoltaic unit at time t; P WT (t) is the output power of the wind turbine at time t.

4. A two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field according to claim 3, characterized in that: The constraints of the load optimization layer model include: Operational constraints for oil and gas production and extraction systems, including single-well productivity constraints, single-well daily total production constraints, well group daily total production constraints, gathering and transportation pipeline flow constraints, single-well daily opening time constraints, well group opening quantity constraints for each time period, and pumping unit operating power constraints; Operational constraints of the oil and gas production water injection system, including water supply and injection volume balance constraints, injection well pressure constraints, injection station water supply constraints, and injection pump displacement constraints; The operational constraints of the oil and gas production and gathering system include the heat load constraints of the heating furnace, the flow constraints of the water injection pump, and the pressure constraints of the water injection pipeline.

5. A two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field according to claim 4, characterized in that: The operating power constraints of the pumping unit include: P i min ≤P i (t)≤P i max , where P i min and P i max are the minimum and maximum output power of the photovoltaic system respectively.

6. A two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field according to claim 1, characterized in that: The objective function of the generation scheduling model is: Among them, C day is the daily operating cost of the multi-energy microgrid; P Bat (t) is the energy storage output power at time t; P buy (t) and P sell (t) are the electricity purchased from the oilfield power grid and the electricity sold to the oilfield power grid at time t; K PV , K WT and K Bat are the operating costs of photovoltaic units and wind turbines, and the energy storage maintenance costs; K buy (t) and K sell (t) are the electricity prices for purchasing and selling electricity from the oil field power grid at time t; T is the scheduling period.

7. A two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field according to claim 6, characterized in that: The constraints of the generation scheduling model include: Photovoltaic unit power constraints, wind turbine unit power constraints, oil field and grid interaction power constraints, energy storage state of charge (SOC) and energy storage charging and discharging power constraints.

8. A two-layer optimization method for a multi-energy microgrid coupled with source, load and storage in an oil and gas well field according to claim 7, characterized in that: The constraints of the power generation scheduling layer model also include power balance constraints. The power balance constraints include photovoltaic units, wind turbines, energy storage batteries and oil field power grids as power sources, and their output power meets the power demand of oil and gas production mining systems, oil and gas production water injection systems and oil and gas production gathering and transportation systems.

9. A multi-energy microgrid dual-layer optimization system for oil and gas well fields with source, load and storage coupling, characterized by: include: The load optimization unit, based on oil and gas production demand and equipment operating parameter constraints, builds a load optimization layer model with the goal of maximizing the green electricity consumption rate, and solves the equipment operation scheduling plan; The power generation dispatch unit, based on the power load data generated by the operation and dispatch plan, combines wind and solar power generation power forecast data and oil field power grid time-of-use electricity price signals to build a power generation dispatch layer model with the goal of minimizing the daily operating cost of the multi-energy microgrid; The coupling unit solves the load optimization layer model and the power generation scheduling layer model through model coupling, and obtains the optimal scheduling plan that includes equipment start-stop timing, energy storage charging and discharging strategy, and oilfield-grid interaction power.

10. A multi-energy microgrid dual-layer optimization system for oil and gas well fields with source-load-storage coupling according to claim 9, characterized in that: Multi-energy microgrid includes: The energy supply subsystem includes photovoltaic units, wind turbines, and an oilfield power grid. The photovoltaic units and wind turbines are connected to the DC bus through a grid-connected controller, and the oilfield power grid is connected to the DC bus through a transformer. Production load subsystem, the pumping units of the production load subsystem are connected to the DC bus through an inverter; Energy storage subsystem, energy storage subsystem and energy supply subsystem are connected; Microgrid control cabinet, the microgrid control cabinet executes the optimal scheduling plan in real time through the energy interlocking decision maker.

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